Energy storage application in low‐voltage microgrids for energy management and power quality improvement

Wasiak, I.; Pawełek, R.; Mieński, R.

doi:10.1049/iet-gtd.2012.0687

Cited by 63 publications

(37 citation statements)

References 16 publications

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“…The ESS are used to increase the network capacity for accepting new wind turbines [10], voltage regulation [11], maximizing revenue for non-firm distributed wind generation [12], energy management and power quality improvement [13] and loss reduction [10]. DR actions can also bring ancillary services to the grid [14], voltage control [15], active loss reduction [16] and better exploitation of renewable energy sources as well as a reduction of the customers' energy consumption costs with both economic and environmental benefits [17].…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal DR and ESS Scheduling for Distribution Losses Payments Minimization Under Electricity Price Uncertainty

Soroudi

Siano

Keane

2016

IEEE Trans. Smart Grid

134

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Section: B Literature Reviewmentioning

confidence: 99%

Optimal DR and ESS Scheduling for Distribution Losses Payments Minimization Under Electricity Price Uncertainty

Soroudi

Siano

Keane

2016

IEEE Trans. Smart Grid

134

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“…Implementation of energy storage can improve power system performance in a variety of ways [12]. Improving dynamic stability [13], transient voltage dips, dynamic voltage stability [14], energy and ancillary service market performance [15], power quality [16], etc. are some of these improvements.…”

Section: Sets and Indices Gmentioning

confidence: 99%

Robust computational framework for mid‐term techno‐economical assessment of energy storage

Maghouli¹,

Soroudi

Keane

2016

IET Generation, Transmission & Distribution

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Abstract-Rapid expansion and integration of wind energy is restrained due to transmission capacity constraints and conventional generation technologies limited operational flexibility in todays power systems. Energy storage is an attractive option to integrate and utilize more renewable energy without major and timely upgrade of existing transmission infrastructure. Also it can be considered as a mean for differing the reinforcement plans. The evaluation of energy storage deployment projects is a challenging task due to severe uncertainty of wind power generation. In this paper a robust techno-economic framework is proposed for energy storage evaluation based on Information Gap Decision Theory for handling wind generation uncertainty. The total social cost of the system including conventional generators' fuel and pollution cost and wind power curtailment cost is optimized considering generators operational constraints and transmission system capacity limitations based on the DC model of the power grid. The effect of storage devices on system performance is evaluated taking into account wind power uncertainty. The proposed method is conducted on the modified IEEE Reliability Test System (IEEE-RTS) and the modified IEEE 118 bus test system to assess its applicability and performance in midterm robust evaluation of energy storage implementation plans.Index Terms-DC optimal power flow, Storage devices, Transmission capacity constraints, Wind power generation, Information Gap Decision Theory. NOMENCLATURE Sets & Indices Capacity of Wind turbine installed in busCharging/discharging efficiency of the energy storage unitCritical/opportunistic increase/decrease of base cost in RA/RS approach ($) ∆t Duration of time period t ag/bg/cg Fuel cost coefficient of thermal unit g P I. INTRODUCTION A. Background and motivationsMany countries have long term plans to increase the share of renewable energy resources in their generation mix. Among these renewable resources there has been increasing interest to invest and integrate more wind power in to existing power systems. However, increasing the wind penetration introduces profound challenges to traditional operating and planning practices. These new challenges arise mainly because of the variable nature of wind energy which results in none optimal operation of electric power systems. Traditionally it is accepted that electricity supply and consumption must remain in instantaneous balance the opportunity to store electrical energy in storage devices may now change this paradigm. Wind power intermittency and variability requires other fast ramping power sources to balance the supply-demand equilibrium in power systems [1]. Because of limiting capacity of fast ramping generation technologies such as hydro and pumped storage power plants, significant integration of wind power to existing power infrastructure requires major system upgrades or implementing energy storage facilities [2]. Besides, using energy storage facilities will result in more utilization of existing assets and ...

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“…Then, the combination of Equations (6) and (7) contributed to Equation (8), the relationship between power deviation and voltage deviation.…”

Section: Relationship Between Load Smoothing and Voltage Deviationmentioning

confidence: 99%

Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration

et al. 2015

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Abstract:With global conventional energy depletion, as well as environmental pollution, utilizing renewable energy for power supply is the only way for human beings to survive. Currently, distributed generation incorporated into a distribution network has become the new trend, with the advantages of controllability, flexibility and tremendous potential. However, the fluctuation of distributed energy resources (DERs) is still the main concern for accurate deployment. Thus, a battery energy storage system (BESS) has to be involved to mitigate the bad effects of DERs' integration. In this paper, optimal scheduling strategies for BESS operation have been proposed, to assist with consuming the renewable energy, reduce the active power loss, alleviate the voltage fluctuation and minimize the electricity cost. Besides, the electric vehicles (EVs) considered as the auxiliary technique are also introduced to attenuate the DERs' influence. Moreover, both day-ahead and real-time operation scheduling strategies were presented under the consideration with the constraints of BESS and the EVs' operation, and the optimization was tackled by a fuzzy mathematical method and an improved particle swarm optimization (IPSO) algorithm. Furthermore, the test system for the proposed strategies is a real distribution network with renewable energy integration.

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Energy storage application in low‐voltage microgrids for energy management and power quality improvement

Cited by 63 publications

References 16 publications

Optimal DR and ESS Scheduling for Distribution Losses Payments Minimization Under Electricity Price Uncertainty

Optimal DR and ESS Scheduling for Distribution Losses Payments Minimization Under Electricity Price Uncertainty

Robust computational framework for mid‐term techno‐economical assessment of energy storage

Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration

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